Improved search algorithms and scoring functions are required before the identification of peptide tandem MS data can be considered to be fully reliable and automatable. The development of models that can accurately predict product ion spectra from a peptide sequence would certainly help achieve this goal, but this firstly requires a better understanding of the process of fragmentation of peptides in the gas-phase. Several studies based on different statistical methods have been conducted to identify factors that can influence the fragmentation. We present a method based on mixed-effects models to identify sequence-dependent fragmentation patterns and show how these results can be used to predict product ion spectra.
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